Documentation

[tlienart]

(see branch docs)

• the fact that, when we can, we avoid doing hcat(X, 1)
• use IterativeSolvers.cg and not IterativeSolvers.lsqr as it allows specifying the operator as a linear map which is efficient and avoids copying when having to add a column for X; anyway it should be identical apart from pathological cases
• robust regression with concomitant scale estimation, not done yet
• LAD/quantile explain weight clipping for pathological zero residual
• generally detail how algos do IWLS etc so that it's 100% clear what is being done
  • Newton
  • NewtonCG (explain briefly then link to Optim)
  • LBFGS (explain briefly then link to Optim)
  • IWLSCG

[CameronBieganek]

Some documentation for the model types would also be handy. This is not very informative:

help?> LogisticClassifier
search: LogisticClassifier

  No documentation found.

  Summary
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  mutable struct LogisticClassifier <: MLJModelInterface.Probabilistic


  Fields
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  lambda             :: Real
  gamma              :: Real
  penalty            :: Union{String, Symbol}
  fit_intercept      :: Bool
  penalize_intercept :: Bool
  solver             :: Union{Nothing, MLJLinearModels.Solver}
  multi_class        :: Bool


  Supertype Hierarchy
  ≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡

  LogisticClassifier <: MLJModelInterface.Probabilistic <: MLJModelInterface.Supervised <: MLJModelInterface.Model <: MLJModelInterface.MLJType <: Any

[tlienart]

yup :)

[tlienart]

@CameronBieganek docs have been added, also docstrings, all pretty rudimentary but hopefully should get the ball going, help / comments very welcome

[CameronBieganek]

Awesome, thanks!